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Found 19 papers, 2 papers with code
Distilling Text Style Transfer With Self-Explanation From LLMs
Text Style Transfer (TST) seeks to alter the style of text while retaining its core content.
Self Expanding Convolutional Neural Networks
In this paper, we present a novel method for dynamically expanding Convolutional Neural Networks (CNNs) during training, aimed at meeting the increasing demand for efficient and sustainable deep learning models.
Volatility models in practice: Rough, Path-dependent or Markovian?
On the positive side: our study identifies a (non-rough) path-dependent Bergomi model and an under-parametrized two-factor Markovian Bergomi model that consistently outperform their rough counterpart in capturing SPX smiles between one week and three years with only 3 to 4 calibratable parameters.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening
We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features.
A Cross-direction Task Decoupling Network for Small Logo Detection
The aggregation of small logos also brings a great challenge to the classification and localization of logos.
The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles
The quintic Ornstein-Uhlenbeck volatility model is a stochastic volatility model where the volatility process is a polynomial function of degree five of a single Ornstein-Uhlenbeck process with fast mean reversion and large vol-of-vol.
Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints
We consider the joint SPX-VIX calibration within a general class of Gaussian polynomial volatility models in which the volatility of the SPX is assumed to be a polynomial function of a Gaussian Volterra process defined as a stochastic convolution between a kernel and a Brownian motion.
Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties.
Parameter Identification of DC Motor based on Compound Least Square Method
For forgetting factor least square identification results are prone to volatility of faults, by introducing selection control, this paper proposes a composite method of least square motor parameter identification, when the motor parameters change, using the composite method of least squares, the real-time identification of dc motor parameters could be faster, more accurate and more stable.
Learned Indexes for a Google-scale Disk-based Database
There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees.
Data Troubles in Sentence Level Confidence Estimation for Machine Translation
For an English-Spanish translation model operating at $SACC = 0. 89$ according to a non-expert annotator pool we can derive a confidence estimate that labels 0. 5-0. 6 of the $good$ translations in an "in-domain" test set with 0. 95 Precision.
AlphaZero Based Post-Storm Repair Crew Dispatch for Distribution Grid Restoration
Natural disasters such as storms usually bring significant damages to distribution grids.
Practical Perspectives on Quality Estimation for Machine Translation
Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output.
An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications.
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data.
Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation
Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips.
Emerging Technologies Cryptography and Security
Spintronics based Stochastic Computing for Efficient Bayesian Inference System
Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness.
